A computer tomography (CT) scan is three-dimensional (3D) image of at least a portion of a subject's body. The scan is a 3D array of integers each of which is the gray level of a body volume element called a voxel. Each voxel in the scan is addressed by means of a triplet of integral coordinates (x,y,z). A voxel in a 2D array or in an image is referred to as a “pixel”. The distance between adjacent voxels along the coordinate axes are referred to as “the x-mm-per-pixel”, the “y-mm-per-pixel” and the “z-mm-per-pixel”. The x- y- and z-mm-per-pixel are usually constant for a specific scan. The x- and y-mm-per-pixel are usually equal in which case they are both referred to simply as the “mm-per-pixel”. The x- and y-dimensions of a scan are a property of the scanner and are usually equal, having a value of 512 pixels for most scanners. The z axis is conventionally taken to be parallel to the longitudinal axis of the subject's body, the y axis directed upwards, and the x axis perpendicular to the y and z axes. A two dimensional (2D) section of the 3D image parallel to one of the coordinate axes is referred to as a “section” of the image. With this convention, in a scan of a supine subject, a (2D) slice of the scan that is parallel to the x, y and z axis is a 2D image of a saggital, coronal and axial section, respectively, of the subject's body. An axial section is also referred to as a “slice” of the scan.
The scan data usually also include the intercept and slope of the scanner. These two numbers are used to convert the gray level values of a specific scan to standard units of gray levels (given in Hounsfields):Values in Hounsfields=slope*scanner output values−intercept.
All gray level values are given hereinafter in Hounsfields (i.e. after conversion to standard units).
A CT scan of a particular organ or tissue of interest will typically include portions of tissues that are adjacent to the organ or tissue of interest. For example, a CT scan of the heart will include organs and tissues adjacent to the heart. The term “cage” is used to refer to non-heart tissues included in a cardiac CT scan and usually includes portions of the lungs, ribs, sternum and liver. In order to analyze a CT scan, it is desirable to remove from the collected data those tissues and organs that are not part of the organ or tissue of interest. This entails locating boundaries between the organ or tissue of interest and surrounding tissues. Once the boundaries of the tissue or organ of interest have been determined, organs and tissues surrounding the tissue or organ of interest can be removed from the scan, a process known as “segmentation” of the data. Segmenting a given CT scan involves determining, for each of one or more voxels in the scan, whether or not the voxel belongs to the tissue or organ of interest. An attribute of 0 or 1 is assigned to each voxel in the scan that specifies whether the voxel belongs to the tissue of interest, or not, respectively. This data is held in a data structure that consists of a three-dimensional array of 0's and 1's, of the same integer dimensions as the scan.
Automatic detection of a boundary between adjacent organs or tissues in a scan is complicated by several factors. For example, removing the cage from a cardiac CT scan is complicated, first of all, by the fact that the range of gray values of cardiac muscle differs from one scan to another, mainly due to variability among subjects. Thus, automatic separation of the heart tissue from surrounding tissues based upon gray levels requires that the range of gray levels of the heart muscle in a given scan be determined. Furthermore, separation of the liver from the heart muscle is difficult because these two tissues have very similar ranges of gray values. The segmentation needs to be precise while avoiding cutting off heart parts, especially any part of the coronary arteries, the analysis of which is one of the main purposes of a heart scan. This is especially difficult when removing the liver.